Exploring the Impact of Image Downscaling Algorithms on Color Perception

Several image downscaling algorithms have been developed over the years, yet their impact on color perception after downscaling has not been thoroughly investigated. This study conducts various experiments to evaluate traditional and modern image downscaling algorithms and their effects on color preservation. Using the adapted method of Quantitative Color Pattern Analysis (QCPA), we objectively measure each algorithm's performance in maintaining color perception. The algorithms compared include Bicubic Interpolation, Lanczos Resampling, Seam Carving, and Rapid Detail-Preserving Image Downscaling (RDPID) using the Imagenette and DIV2K datasets. Evaluation metrics encompass average color histogram similarity, dominant color shifts, and color scatter plots. Results reveal varying degrees of color information loss among the algorithms, with Bicubic Interpolation and RDPID showing superior color fidelity compared to Seam Carving. For the Imagenette dataset, average color histogram similarity between the original and downscaled images is 78.9% for Bicubic Interpolation, 75.8% Lanczos Resampling, 77.6% for RDPID, and 20.8% for Seam Carving. For the DIV2K dataset, the similarities are 65.7% for Bicubic Interpolation, 64.7% for RDPID, and 48.8% for Seam Carving. This research highlights the extent of color information loss due to the downscaling process across the chosen algorithms. Future studies should explore additional algorithms and assess their performance across diverse datasets and applications, enhancing our understanding of color information loss in image downscaling and its impact on computer vision tasks.